1. Field of the Invention
The invention relates to a method for verification of signatures and handwriting which includes a neural net for image recognition.
2. Description of Related Art
There are two general principles for comparison of handwriting and signatures, the dynamic and the static comparison. For a dynamic verification, like the biometrics based verification in the IBM Transaction Security System, the originator must be physically present. The static approach can be used also in environments where an image of a signature or handwriting must be checked without the physically present originator, for example cheque processing.
A simple image comparison matches the two given images on a picture element basis. This may include a sizing and rotation operation to compensate differences in image resolutions and skews. In this case it is very hard to compensate variations in the geometry of the image contents itself without adulterating it. A simple feature comparison will be achieved by comparing the sample features against the reference features and calculating the difference between them. The identification of a measurement is the main problem in that case.
An example of a technique based on the extraction and comparison of significant features and starting point of the present invention is the one described in EP-A-0 483 391. The feature extraction leads to a significant reduction of storage space and calculation time needed. For the image capture process a scanner or a touch-sensitive device can be used. From the binary representation of the rastered image, the actual features are computed and combined to a vector of feature values, called a feature set. For a comparison of two images only the two feature sets are compared. The image information is not needed any longer. To compare two feature sets, each feature is compared and weighted separately. To find good weightings for this comparison is extremely difficult. For that, an artificial neural net approach can be used. The arithmetic differences between each two corresponding features from all features of the feature sets is calculated and fed into the neural net. There they are weighted internally and an output is calculated which gives a value to be interpreted as the probability whether the two images match. A well trained neural net will be able to classify not only images used during training but also those which are presented the first time at all. Using a state of the art artificial neural network, like the multi-layer perceptron, to compare two sample images, recognition rates up to 95 percent have been achieved.